On the utility of content analysis in author attribution:The Federalist

Martindale, Colin; McKenzie, Dean Philip

doi:10.1007/bf01830395

Cited by 51 publications

(22 citation statements)

References 21 publications

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“…For example, one author may prefer to use the words start and large, where another may prefer begin and big (Mosteller andWallace 1964, Koppel et al 2006a). Such patterns of lexical choice can be represented by modeling the relative frequencies of content words (Martindale and McKenzie 1995;Craig 1999;Waugh et al 2000;Diederich et al 2003;Hoover 2004aHoover , 2004bArgamon et al 2008). Typically very rare words and those with near-uniform distribution over the corpus of interest can be omitted (Forman 2003), so that a reasonable set of perhaps several thousand words may used.…”

Section: Content Wordsmentioning

confidence: 99%

Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

462

278

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Statistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each. Real-life authorship attribution problems, however, typically fall short of this ideal. Thus, following detailed discussion of previous work, three scenarios are considered here for which solutions to the basic attribution problem are inadequate. In the first variant, the profiling problem, there is no candidate set at all; in this case, the challenge is to provide as much demographic or psychological information as possible about the author. In the second variant, the needle-in-a-haystack problem, there are many thousands of candidates for each of whom we might have a very limited writing sample.In the third variant, the verification problem, there is no closed candidate set but there is one suspect; in this case, the challenge is to determine if the suspect is or is not the author. For each variant, it is shown how machine learning methods can be adapted to handle the special challenges of that variant.

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Section: Content Wordsmentioning

confidence: 99%

Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

462

278

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“…Studies include [58,99,122,142]; Rudman [127] lists no less than nineteen studies of this particular corpus and is hardly complete. Perhaps needless to say, almost all of these studies confirm this particular assignment of authorship and the correctness of Mosteller and Wallace's results.…”

Section: The Federalist Analysesmentioning

confidence: 99%

Authorship Attribution

Juola

2007

FNT in Information Retrieval

468

232

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Authorship attribution, the science of inferring characteristics of the author from the characteristics of documents written by that author, is a problem with a long history and a wide range of application. Recent work in "non-traditional" authorship attribution demonstrates the practicality of automatically analyzing documents based on authorial style, but the state of the art is confusing. Analyses are difficult to apply, little is known about type or rate of errors, and few "best practices" are available. In part because of this confusion, the field has perhaps had less uptake and general acceptance than is its due.This review surveys the history and present state of the discipline, presenting some comparative results when available. It shows, first, that the discipline is quite successful, even in difficult cases involving small documents in unfamiliar and less studied languages; it further analyzes the types of analysis and features used and tries to determine characteristics of well-performing systems, finally formulating these in a set of recommendations for best practices.

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“…Mosteller andWallace (1964, 1984) carried extensive comparisons of the frequencies of a carefully chosen set of common words in writings known to be by Hamilton and by Madison, with the frequencies of these words in the twelve disputed papers. Recent studies re-visiting that problem are, for example, Holmes and Forsyth (1995), Martindale and McKenzie (1995), Tweedie et al (1996), Bosch and Smith (1998), Khmelev and Tweedie (2001), Collins et al (2004), and Jockers and Witten (2010).…”

Section: Authorship Attribution Case Studymentioning

confidence: 99%

A Unified Approach to Authorship Attribution and Verification

Puig¹,

Font²,

Ginebra³

2016

The American Statistician

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In authorship attribution one assigns texts from an unknown author to either one of two or more candidate authors by comparing the disputed texts with texts known to have been written by the candidate authors. In authorship verification one decides whether a text or a set of texts could have been written by a given author. These two problems are usually treated separately. By assuming an open-set classification framework for the attribution problem, contemplating the possibility that none of the candidate authors is the unknown author, the verification problem becomes a special case of attribution problem. Here both problems are posed as a formal Bayesian multinomial model selection problem and are given a closed form solution, tailored for categorical data, naturally incorporating text length and dependence in the analysis, and coping well with settings with a small number of training texts. The approach to authorship verification is illustrated by exploring whether a court ruling sentence could have been written by the judge that signs it, and the approach to authorship attribution is illustrated by revisiting the authorship attribution of the Federalist papers and through a small simulation study.

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On the utility of content analysis in author attribution:The Federalist

Cited by 51 publications

References 21 publications

Computational methods in authorship attribution

Computational methods in authorship attribution

Authorship Attribution

A Unified Approach to Authorship Attribution and Verification

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